Flavour Tagging with Graph Neural Network with the ATLAS Detector

The identification of jets containing b-hadrons is key to many physics analyses at the LHC, including measurements involving Higgs bosons or top quarks, and searches for physics beyond the Standard Model. In this contribution, the most recent enhancements in the capability of ATLAS to separate b-jet...

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Detalles Bibliográficos
Autor principal: Khanov, Alexander
Lenguaje:eng
Publicado: 2023
Materias:
Particle Physics - Experiment
Acceso en línea:http://cds.cern.ch/record/2876361
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author Khanov, Alexander
author_facet Khanov, Alexander
author_sort Khanov, Alexander
collection CERN
description The identification of jets containing b-hadrons is key to many physics analyses at the LHC, including measurements involving Higgs bosons or top quarks, and searches for physics beyond the Standard Model. In this contribution, the most recent enhancements in the capability of ATLAS to separate b-jets from jets stemming from lighter quarks will be presented. The improved performance originates from the usage of state-of-the-art machine learning algorithms based on graph networks. A factor of more than 2 to reject light- and c-quark-initiated jet is observed compared to the current performance. The expected performance of this algorithm at the High-Luminosity LHC (HL-LHC) will also be discussed in detail.
id cern-2876361
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28763612023-10-20T19:55:41Zhttp://cds.cern.ch/record/2876361engKhanov, AlexanderFlavour Tagging with Graph Neural Network with the ATLAS DetectorParticle Physics - ExperimentThe identification of jets containing b-hadrons is key to many physics analyses at the LHC, including measurements involving Higgs bosons or top quarks, and searches for physics beyond the Standard Model. In this contribution, the most recent enhancements in the capability of ATLAS to separate b-jets from jets stemming from lighter quarks will be presented. The improved performance originates from the usage of state-of-the-art machine learning algorithms based on graph networks. A factor of more than 2 to reject light- and c-quark-initiated jet is observed compared to the current performance. The expected performance of this algorithm at the High-Luminosity LHC (HL-LHC) will also be discussed in detail.ATL-PHYS-SLIDE-2023-581oai:cds.cern.ch:28763612023-10-20
spellingShingle Particle Physics - Experiment
Khanov, Alexander
Flavour Tagging with Graph Neural Network with the ATLAS Detector
title Flavour Tagging with Graph Neural Network with the ATLAS Detector
title_full Flavour Tagging with Graph Neural Network with the ATLAS Detector
title_fullStr Flavour Tagging with Graph Neural Network with the ATLAS Detector
title_full_unstemmed Flavour Tagging with Graph Neural Network with the ATLAS Detector
title_short Flavour Tagging with Graph Neural Network with the ATLAS Detector
title_sort flavour tagging with graph neural network with the atlas detector
topic Particle Physics - Experiment
url http://cds.cern.ch/record/2876361
work_keys_str_mv AT khanovalexander flavourtaggingwithgraphneuralnetworkwiththeatlasdetector

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